公开(公告)号：US20130163858A1

公开(公告)日：2013-06-27

申请号：US13545629

申请日：2012-07-10

申请人： Kye Kyung KIM ,  Woo Han YUN ,  Hye Jin KIM ,  Su Young CHI ,  Jae Yeon LEE ,  Mun Sung HAN ,  Jae Hong KIM ,  Joo Chan SOHN

发明人： Kye Kyung KIM ,  Woo Han YUN ,  Hye Jin KIM ,  Su Young CHI ,  Jae Yeon LEE ,  Mun Sung HAN ,  Jae Hong KIM ,  Joo Chan SOHN

IPC分类号： G06K9/62

CPC分类号： G06K9/00 ,  G06K2209/19 ,  G06T7/12 ,  G06T7/74 ,  G06T2207/30164

摘要： Disclosed are a component recognizing apparatus and a component recognizing method. The component recognizing apparatus includes: an image preprocessing unit configured to extract component edges from an input component image by using a plurality of edge detecting techniques, and detect a component region by using the extracted component edges; a feature extracting unit configured to extract a component feature from the detected component region, and create a feature vector by using the component feature; and a component recognizing unit configured to input the created feature vector to an artificial neural network which has learned in advance to recognize a component category through a plurality of component image samples, and recognize the component category according to a result.

摘要翻译： 公开了一种部件识别装置和部件识别方法。 该分量识别装置包括：图像预处理单元，被配置为通过使用多个边缘检测技术从输入分量图像中提取分量边缘，并且通过使用所提取的分量边缘来检测分量区域; 特征提取单元，被配置为从检测到的分量区域中提取分量特征，并通过使用分量特征来创建特征向量; 以及分量识别单元，被配置为将所创建的特征向量输入到通过多个分量图像样本预先学习识别分量类别的人造神经网络，并根据结果识别组件类别。

公开(公告)号：US20130163829A1

公开(公告)日：2013-06-27

申请号：US13565022

申请日：2012-08-02

申请人： Kye Kyung KIM ,  Jae Yeon LEE ,  Ho Sub YOON ,  Jae Hong KIM ,  Joo Chan SOHN

发明人： Kye Kyung KIM ,  Jae Yeon LEE ,  Ho Sub YOON ,  Jae Hong KIM ,  Joo Chan SOHN

IPC分类号： G06K9/62

CPC分类号： G06K9/00 ,  G06K9/00288 ,  G06K9/00899

摘要： Disclosed are a system and a method for recognizing a disguised face using a Gabor feature and a support vector machine (SVM) classifier according to the present invention.The system for recognizing a disguised face includes: a graph generation means to generate a single standard face graph from a plurality of facial image samples; a support vector machine (SVM) learning means to determine an optimal classification plane for discriminating a disguised face from the plurality of facial image samples and disguised facial image samples; and a facial recognition means to determine whether an input facial image is disguised using the standard face graph and the optimal classification plane when the facial image to be recognized is input.

摘要翻译： 公开了根据本发明的使用Gabor特征和支持向量机（SVM）分类器识别伪装脸部的系统和方法。 用于识别伪装脸部的系统包括：图形生成装置，用于从多个面部图像样本生成单个标准面部图形; 支持向量机（SVM）学习装置，用于确定用于从多个面部图像样本和伪装的面部图像样本中鉴别伪装的脸部的最佳分类平面; 以及面部识别装置，用于当要输入要识别的面部图像时，使用标准面图和最佳分类平面来确定输入面部图像是否伪装。